Assessing the Influence of Roads on Fire Ignition: Does Land Cover Matter?

Ricotta, Carlo; Bajocco, Sofia; Guglietta, Daniela; Conedera, Marco

doi:10.3390/fire1020024

Cited by 46 publications

(31 citation statements)

References 42 publications

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“…In the European Mediterranean areas, about 48,600 fires burn around 447,800 ha every year (1980–2015) and the few large fires that escape from initial attack account for the bulk of the burned areas (San‐Miguel‐Ayanz et al., 2017). These catastrophic events are mostly caused by humans, often ignite nearby urban areas or roads, spread for long distances, expose a large number of structures, and affect valued natural resources (Ager, Preisler, Arca, Spano, & Salis, 2014; Sergi Costafreda‐Aumedes, Comas, & Vega‐Garcia, 2017; Ricotta, Bajocco, Guglietta, & Conedera, 2018; Salis, Ager, Finney, Arca, & Spano, 2013). Indeed, the last 15 years have witnessed some of the most damaging wildfires ever experienced in southern EU countries, which have caused numerous causalties and substantial economic losses despite increasing investments in fire suppression (Cardil, Delogu, & Molina‐Terran, 2017; Moritz et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region

et al. 2020

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Despite escalating expenditures in firefighting, extreme fire events continue to pose a major threat to ecosystem services and human communities in Mediterranean areas. Developing a safe and effective fire response is paramount to efficiently restrict fire spread, reduce negative effects to natural values, prevent residential housing losses, and avoid causalties. Though current fire policies in most countries demand full suppression, few studies have attempted to identify the strategic locations where firefighting efforts would likely contain catastrophic fire events. The success in containing those fires that escape initial attack is determined by diverse structural factors such as ground accessibility, airborne support, barriers to surface fire spread, and vegetation impedance. In this study, we predicted the success in fire containment across Catalonia (northeastern Spain) using a model generated with random forest from detailed geospatial data and a set of 73 fire perimeters for the period 2008-2016. The model attained a high predictive performance (AUC = 0.88), and the results were provided at fine resolution (25 m) for the entire study area (32,108 km 2). The highest success rates were found in agricultural plains along the nonburnable barriers such as major road corridors and largest rivers. Low levels of containment likelihood were predicted for dense forest lands and steep-relief mountainous areas. The results can assist in suppression resource pre-positioning and extended attack decision making, but also in strategic fuels management oriented at creating defensive locations and fragmenting the landscape in operational firefighting areas. Our modeling workflow and methods may serve as a baseline to generate locally adapted models in fire-prone areas elsewhere.

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Section: Introductionmentioning

confidence: 99%

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region

et al. 2020

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“…Previous studies have shown that annual mean temperature has a strong relationship with the number of res in Golestan Province (Eskandari 2015). Furthermore, the proximity to roads has also been identi ed as important factor in re susceptibility potential (Martinez et al 2009; Narayanaraj and Wimberly 2011; Rodrigues et al 2016;Eskandari and Miesel 2017;Ricotta et al 2018;Eskandari et al 2020). Wind effect has also been identi ed as an important effective factor in re occurrence (Tymstra et al 2007;Jolly et al 2015;.…”

Section: Multi-collinearity Analysismentioning

confidence: 98%

Fire Susceptibility Mapping in the Northeast Forests and Rangelands of Iran using New and Ensemble Data Mining Models

Eskandari

Pourghasemi

Tiefenbacher³

2021

Preprint

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Fires have increased in the northeastern Iran as its semiarid climate landscape is being desiccated by human activities. To combat fire outbreaks in any region, one must map fire susceptibility with accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of northeastern Iran’s Golestan Province using new data mining models. Fire effective factors data describing elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance from river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined with a random forest algorithm. Fire susceptibility maps were produced in R 3.3.3 software using GAM, MARS, SVM algorithms and a new ensemble of the three models: GAM-MARS-SVM. Validation of the four fire susceptibility maps was performed with the area under the curve. Results show that distance from village, annual mean rainfall and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest fire susceptibility mapping precision. The fire susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province.

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“…As a proxy of anthropogenic activities, the distance to roads was adopted, with values ranging from 0, for areas at a distance of 200 m or more from roads, to 1 for areas at immediate proximity to them. Distance to roads has been reported to be positively associated with ignition frequency [67,68]. The Pyric History does not have a direct relationship with fire hazard, especially if one takes into account that a past wildfire may reduce the fuel load and subsequently the intensity of a fire event.…”

Section: Of 23mentioning

confidence: 99%

Integrating Remote Sensing Methods and Fire Simulation Models to Estimate Fire Hazard in a South-East Mediterranean Protected Area

Xofis

Konstantinidis²,

Papadopoulos³

et al. 2020

Fire

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Unlike low intensity fire which promotes landscape heterogeneity and important ecosystem services, large high-intensity wildfires constitute a significant destructive factor despite the increased amount of resources allocated to fire suppression and the improvement of firefighting tactics and levels of organization. Wildfires also affect properties, while an increasing number of fatalities are also associated with wildfires. It is now widely accepted that an effective wildfire management strategy can no longer rely on fire suppression alone. Scientific advances on fire behavior simulation and the increasing availability of remote sensing data, along with advanced systems of fire detection can significantly reduce fire hazards. In the current study remote sensing data and methods, and fire behavior simulation models are integrated to assess the fire hazard in a protected area of the southeast Mediterranean region and its surroundings. A spatially explicit fire hazard index was generated by combining fire intensity estimations and proxies of fire ignition probability. The results suggest that more than 50% of the study area, and the great majority of the protected area, is facing an extremely high hazard for a high-intensity fire. Pine forest formations, characterized by high flammability, low canopy base height and a dense shrub understory are facing the most critical hazard. The results are discussed in relation to the need for adopting an alternative wildfire management strategy.

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Assessing the Influence of Roads on Fire Ignition: Does Land Cover Matter?

Cited by 46 publications

References 42 publications

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region

Fire Susceptibility Mapping in the Northeast Forests and Rangelands of Iran using New and Ensemble Data Mining Models

Integrating Remote Sensing Methods and Fire Simulation Models to Estimate Fire Hazard in a South-East Mediterranean Protected Area

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